bioRxiv Science⌕ Search

Biology subjects

Dufies, M.

Publications and source records attributed to Dufies, M..

2 recordsLinked to original sources

Disrupting USP39 deubiquitinase Function Impairs the Survival and Migration of Multiple Myeloma Cells through ZEB1 Degradation

RationaleMultiple Myeloma (MM) stands as the second most common hematological malignancy characterized by the accumulation of monoclonal plasmocytes within the bone marrow. Despite the introduction of proteasome inhibitors, immunomodulatory agents and CD38-targeting antibodies which have extended survival rates, the disease remains incurable for most patients due to the emergence of resistant clones and frequent relapses. The efficacy of the proteasome inhibitor bortezomib (BTZ) in MM treatment underscores the critical role of the ubiquitin proteasome system (UPS) in this cancer. Deubiquitinases (DUBs), a class of enzymes governing the stability, interactions or localization of cellular proteins by removing ubiquitin modifications, have emerged as promising therapeutic targets across various cancers, including MM. MethodsThrough an exhaustive loss-of-function approach, we have identified for the first time USP39 DUB as a pivotal survival determinant for MM cells. ResultsOur analysis reveals a direct correlation between heightened USP39 mRNA levels and shorter survival in MM patients. Additionally, robust USP39 protein expression is observed in MM patient plasmocytes compared to healthy counterparts. Knockdown of Usp39 not only impedes clonogenic capabilities, but also induces apoptosis, triggers cell cycle arrest and overcomes BTZ resistance. Complementary gain-of-function assays, further elucidate how USP39, by stabilizing the transcription factor ZEB1, enhances the trans-migratory potential of MM cells. ConclusionsIn summary, our findings underscores the pivotal role of the deubiquitinase USP39, suggesting that targeting the USP39/ZEB1 axis hold promise as a prospective diagnostic marker and therapeutic target in MM.

cancer biology↗

A multi-layered systems approach for renal cell carcinoma

Renal cell carcinoma (RCC) still lacks prognostic and predictive biomarkers to monitor the disease and the response to therapy. The usual strategy in translational research is to start from human samples, to identify molecular markers and gene networks and then to functionally validate them in vitro and in animal models. We devised herein a completely opposite strategy from "mouse to man" by performing an aggressiveness screen and used functional genomics, imaging, clinical data and computational approaches in order to discover molecular pathways and players in renal cancer development and metastasis. Multiple cell lines for primary tumor growth, survival in the blood circulation and lung metastasis or metastatic spread from the primary tumor were generated and analyzed using a multi-layered approach which includes large-scale transcriptome, genome and methylome analyses. Transcriptome and methylome analyses demonstrated distinct clustering in three different groups. Remarkably, DNA sequencing did not show significant genomic variations in the different groups which indicates absence of clonal selection during the in vivo amplification process. Transcriptome analysis revealed distinct signatures of tumor aggressiveness which were validated in patient cohorts. Methylome analysis of full-length DNA allowed clustering of the same groups and revealed clinically relevant signatures. Furthermore, we identified SAA2 and CFB as soluble prognostic and predictive biomarkers of the therapeutic response. We also uncovered IL34 as another soluble prognostic biomarker and key regulator of renal cell carcinoma (RCC) progression. This was also functionally validated in vivo, and a mathematical model of IL34-dependent primary tumor growth and metastasis development was provided. These results indicate that such multilayered analysis in a RCC animal model leads to meaningful results that are of translational significance. One Sentence SummaryAn aggressiveness screen with multilayer systems analysis to identify signatures and biomarkers for renal cell carcinoma aggressiveness.

cancer biology↗